Neural Plasticity For Rich And Uncertain Robotic Information Streams
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Neural Plasticity For Rich And Uncertain Robotic Information Streams
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Author : Andrea Soltoggio
language : en
Publisher: Frontiers Media SA
Release Date : 2016-10-31
Neural Plasticity For Rich And Uncertain Robotic Information Streams written by Andrea Soltoggio and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-31 with Neurosciences. Biological psychiatry. Neuropsychiatry categories.
Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.
Neural Plasticity For Rich And Uncertain Robotic Information Streams
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Author :
language : en
Publisher:
Release Date : 2016
Neural Plasticity For Rich And Uncertain Robotic Information Streams written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Models of adaptation and neural plasticity are often demonstrated in robotic scenarios with heavily pre-processed and regulated information streams to provide learning algorithms with appropriate, well timed, and meaningful data to match the assumptions of learning rules. On the contrary, natural scenarios are often rich of raw, asynchronous, overlapping and uncertain inputs and outputs whose relationships and meaning are progressively acquired, disambiguated, and used for further learning. Therefore, recent research efforts focus on neural embodied systems that rely less on well timed and pre-processed inputs, but rather extract autonomously relationships and features in time and space. In particular, realistic and more complete models of plasticity must account for delayed rewards, noisy and ambiguous data, emerging and novel input features during online learning. Such approaches model the progressive acquisition of knowledge into neural systems through experience in environments that may be affected by ambiguities, uncertain signals, delays, or novel features.
Artificial Brain And Simulation
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Author : S. R. Jena
language : en
Publisher: SRJX RESEARCH AND INNOVATION LAB LLP
Release Date : 2025-06-28
Artificial Brain And Simulation written by S. R. Jena and has been published by SRJX RESEARCH AND INNOVATION LAB LLP this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-28 with Computers categories.
The human brain remains the most explosive enigmatic and powerful computing system ever known. Its unmatched ability to learn, adapt, reason, and generate creativity continues to inspire scientists, engineers, and philosophers across generations. With the rise of Artificial Intelligence (AI), Neuroscience, and Neuromorphic Engineering, the question once relegated to the realm of science fiction — Can we build an artificial brain? — is now a legitimate and actively explored scientific frontier. This book, Artificial Brain and Simulation, is an earnest attempt to synthesize the diverse yet interrelated domains of cognitive science, machine learning, brain simulation, neuromorphic computing, and robotics into a cohesive academic framework. This work is not merely a speculative exploration of artificial cognition. Rather, it is grounded in current scientific developments, technological breakthroughs, and practical systems already demonstrating nascent forms of synthetic intelligence. From IBM Watson’s symbolic reasoning to the digital neurons firing inside Intel’s Loihi neuromorphic chip, from brain-computer interfaces used in prosthetics to AI-driven diagnosis of neurological disorders, the world is witnessing an unprecedented convergence of human cognition and machine computation. This convergence is shaping what we refer to as Artificial Brain Simulation. Why This Book? The goal of this book is to serve as a comprehensive guide and reference text for students, researchers, academicians, technologists, and policy makers. It captures the evolving narrative of brain-inspired computing, simulative cognition, and intelligent neural interfaces. Despite the proliferation of literature on AI and neuroscience individually, there exists a noticeable void where both disciplines intersect with engineering design — particularly in the design and simulation of artificial brains. This book addresses that void. It dives deep into the biological fundamentals of the human brain while simultaneously translating those concepts into machine-executable systems, neural network models, and cognitive architectures. It traces the history, evaluates the present, and speculates on the future of artificially simulating human thought, perception, memory, decision-making, emotion, and even consciousness. Target Audience This book is written with multiple tiers of readers in mind: Undergraduate and graduate students studying computer science, neuroscience, AI, robotics, cognitive science, or biomedical engineering. Researchers and Ph.D. candidates seeking deep insights into brain-inspired AI, computational neuroscience, and machine consciousness. Faculty and educators looking for a structured reference to design multidisciplinary courses involving AI and biological cognition. Industry professionals and startups working on neural interfaces, robotics, AR/VR, BCI, IoT, and intelligent automation. Futurists and philosophers of technology interested in the ethical, social, and psychological dimensions of synthetic minds. Book Structure and Flow The book is divided into 14 meticulously crafted chapters, each building upon the foundation laid by its predecessors: Chapter 1: Introduction To Artificial Brain This chapter defines what constitutes an artificial brain, outlines the motivation behind its development, and distinguishes it from general-purpose AI. It provides historical insights and visual representations comparing human brains with machine-based intelligence. Chapter 2: Neuroscience Overview To simulate the brain, one must first understand it. This chapter explains the structure, components, and processes of the human brain — including neurons, synapses, learning, memory, and cognition — all explained in computational terms. Chapter 3: Foundations of Artificial Intelligence This chapter transitions to core AI principles, introducing learning paradigms (supervised, unsupervised, reinforcement), deep learning, neural networks, and cognitive architectures like ACT-R and SOAR. Chapter 4: Neuromorphic Computing Neuromorphic systems mimic the behavior of neurons in silicon. This chapter details spiking neural networks, memristors, neuromorphic chips like Loihi and TrueNorth, and the integration of hardware with software. Chapter 5: Brain-Inspired Algorithms Here we discuss Hebbian learning, reinforcement learning loops, bio-inspired optimization methods (GA, PSO, ACO, BFO), and deep cognitive networks as scalable learning systems. Chapter 6: Brain Simulation Projects This chapter dives into real-world simulations like the Blue Brain Project, the Human Brain Project (HBP), OpenWorm, and Nengo — highlighting architectural details and outcomes. Chapter 7: Architecture of Artificial Brain Covering layer-wise simulation of perception, cognition, decision-making, and motor control, this chapter also introduces architectural block diagrams of artificial brains in modular format. Chapter 8: Cognitive Computing and Reasoning Explores AI capabilities in symbolic reasoning, language understanding, planning, perception, and the philosophical notion of self-awareness in synthetic systems. Chapter 9: Memory and Learning Systems It delves into memory models (short-term vs long-term), neural memory frameworks, lifelong learning, and transfer learning. Visual diagrams illustrate how memory evolves in artificial systems. Chapter 10: AI in Healthcare and Brain-Computer Interfaces (BCIs) The intersection of AI and neuroscience is most visible in neural prosthetics, AI for neurological disorders, and BCI-based medical interventions. This chapter includes real-time BCI system design. Chapter 11: Robotics and Autonomous Systems This chapter introduces cognitive robots, emotion-enabled machines, artificial empathy, and humanoid assistants that simulate real social interaction and decision-making. Chapter 12: Smart Systems and Embedded AI Explores deployment of cognitive systems in mobile chips, IoT platforms, smart surveillance, and AR/VR environments. It underscores the importance of real-time, low-power neural architectures. Chapter 13: Ethical, Philosophical Issues and Technological Challenges As artificial brains grow closer to consciousness, this chapter discusses machine rights, existential risks, interpretability, and the control mechanisms necessary to safeguard humanity. A speculative yet evidence-based chapter exploring machine consciousness, AI-human symbiosis, and the implications of uploading human minds into machines (mind uploading). Chapter 14: The Future of Artificial Brain A forward-looking chapter forecasting technological, cognitive, societal, and regulatory trends shaping the future of artificial brain systems. Unique Features of the Book Interdisciplinary Approach: Merges neuroscience, AI, robotics, cognitive science, ethics, and embedded computing. Detailed Diagrams: Over 100 hand-drawn and digitally illustrated diagrams explain complex systems in accessible formats. Comparison Tables: Comparative evaluations of architectures (e.g., ACT-R vs SOAR), chip designs (Loihi vs TrueNorth), and learning models. Recent Research Citations: Each chapter ends with a list of 30 IEEE-style references covering the most recent developments. Case Studies & Applications: Includes Neuralink, BrainGate, OpenWorm, HBP, and real-world BCI-enabled prosthetics. Ethical & Philosophical Lens: Goes beyond technology to address societal impact, machine rights, and AI regulation. The journey toward building an artificial brain is not merely technological—it is deeply philosophical, neuropsychological, and even spiritual. The idea that machines could one day think, feel, or possess some form of synthetic awareness requires us to redefine intelligence, personhood, and even life itself. This book encourages readers to question conventional boundaries and embrace a future that may include minds made of code, thoughts running through silicon, and humanity coexisting with a new cognitive species. We believe Artificial Brain and Simulation will serve as a bridge — connecting the brilliance of natural intelligence with the promise of artificial cognition. Whether you are a student, researcher, or simply a curious mind, we invite you to embark on this voyage where biology meets computation, neurons inspire algorithms, and thought itself is reimagined. We thank you for picking up this book — and we hope it will both inform and inspire you to shape the intelligent systems of tomorrow.
Surfing Uncertainty
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Author : Andy Clark
language : en
Publisher: Oxford University Press, USA
Release Date : 2016
Surfing Uncertainty written by Andy Clark and has been published by Oxford University Press, USA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Medical categories.
Exciting new theories in neuroscience, psychology, and artificial intelligence are revealing minds like ours as predictive minds, forever trying to guess the incoming streams of sensory stimulation before they arrive. In this up-to-the-minute treatment, philosopher and cognitive scientist Andy Clark explores new ways of thinking about perception, action, and the embodied mind.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Methods For Neural Ensemble Recordings
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Author : Miguel A. L. Nicolelis
language : en
Publisher: CRC Press
Release Date : 2007-12-03
Methods For Neural Ensemble Recordings written by Miguel A. L. Nicolelis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-03 with Medical categories.
Extensively updated and expanded, this second edition of a bestseller distills the current state-of-the-science and provides the nuts and bolts foundation of the methods involved in this rapidly growing science. With contributions from pioneering researchers, it includes microwire array design for chronic neural recordings, new surgical techniques for chronic implantation, microelectrode microstimulation of brain tissue, multielectrode recordings in the somatosensory system and during learning, as well as recordings from the central gustatory-reward pathways. It explores the use of Brain-Machine Interface to restore neurological function and proposes conceptual and technical approaches to human neural ensemble recordings in the future.
Optimization For Machine Learning
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Author : Suvrit Sra
language : en
Publisher: MIT Press
Release Date : 2012
Optimization For Machine Learning written by Suvrit Sra and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Innate
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Author : Kevin J. Mitchell
language : en
Publisher: Princeton University Press
Release Date : 2018-10-16
Innate written by Kevin J. Mitchell and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-16 with Science categories.
A leading neuroscientist explains why your personal traits are more innate than you think What makes you the way you are—and what makes each of us different from everyone else? In Innate, leading neuroscientist and popular science blogger Kevin Mitchell traces human diversity and individual differences to their deepest level: in the wiring of our brains. Deftly guiding us through important new research, including his own groundbreaking work, he explains how variations in the way our brains develop before birth strongly influence our psychology and behavior throughout our lives, shaping our personality, intelligence, sexuality, and even the way we perceive the world. We all share a genetic program for making a human brain, and the program for making a brain like yours is specifically encoded in your DNA. But, as Mitchell explains, the way that program plays out is affected by random processes of development that manifest uniquely in each person, even identical twins. The key insight of Innate is that the combination of these developmental and genetic variations creates innate differences in how our brains are wired—differences that impact all aspects of our psychology—and this insight promises to transform the way we see the interplay of nature and nurture. Innate also explores the genetic and neural underpinnings of disorders such as autism, schizophrenia, and epilepsy, and how our understanding of these conditions is being revolutionized. In addition, the book examines the social and ethical implications of these ideas and of new technologies that may soon offer the means to predict or manipulate human traits. Compelling and original, Innate will change the way you think about why and how we are who we are.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1989
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Aeronautics categories.